How can we create agile micro aerial vehicles that are able to operate autonomously in cluttered indoor and outdoor environments? You will gain an introduction to the mechanics of flight and the design of quadrotor flying robots and will be able to develop dynamic models, derive controllers, and synthesize planners for operating in three dimensional environments. You will be exposed to the challenges of using noisy sensors for localization and maneuvering in complex, three-dimensional environments. Finally, you will gain insights through seeing real world examples of the possible applications and challenges for the rapidly-growing drone industry.
Mathematical prerequisites: Students taking this course are expected to have some familiarity with linear algebra, single variable calculus, and differential equations.
Programming prerequisites: Some experience programming with MATLAB or Octave is recommended (we will use MATLAB in this course.) MATLAB will require the use of a 64-bit computer.

CG

Well balanced mix of theory and practical applicability. Explanation of the material is also very good.\n\nThe assignments are nicely built on the taught material to stimulate understanding.

AJ

Nov 22, 2017

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Exceptional Material, not only are the concept are explained well, the supplementary material are provided for all possible requirement along side. Advance course with minimum prerequisite

De la lección

Introduction to Aerial Robotics

Welcome to Week 1! In this week, you will be introduced to the exciting field of Unmanned Aerial Robotics (UAVs) and quadrotors in particular. You will learn about their basic mechanics and control strategies and realize how careful component selection and design affect the vehicles' performance. This week also provides you with instructions on how to download and install Matlab. This software will be used throughout this course in exercises and assignments, so it is strongly recommended to familiarize yourself with Matlab soon. Tutorials to help you get started are also provided in this week.

Impartido por:

Vijay Kumar

Nemirovsky Family Dean of Penn Engineering and Professor of Mechanical Engineering and Applied Mechanics

Transcripción

Next, I wanna take a look at the components that constitute a robot and then try to analyze the effects of these components on the agility of the robot. And more precisely, we'll be looking at the stopping distance of the robot. So, this chart illustrates some of the design choices you might have if you go online. So I've chosen to visit dji.com and pull off some frames that you can buy off the shelf. DJI is currently the largest manufacturer of drones and they have a wide selection of frames, batteries, propellers and motors. In addition to that, you need an autopilot. Pixhawk is one that's open source and sold by 3D Robotics. You can also buy high level processors such as those made by Intel. You clearly need the autopilot for doing low level control and you need something like the Intel processor you see here to do high level computations. You want to pay particular attention to the weight of each of these because eventually your robot will have to carry these as it flies. So the control architecture, you might think about, involves using this low level processor to drive the motors and the propellers and a high level processor like the Intel which communicates with a lower level processor and commands a low level processor to drive the vehicle. In addition you also want to have something that complements the autonomous system. A radio controller in case you have to take control of the vehicle. So we show standard components here that you can buy off the shelf. This is an example of an outdoor platform that we built. And in this video you will essentially see that we have taken standard off the shelf components, a DJI platform with motors that you can buy from the DJI website with a simulated payload, it's a 600 gram payload and aluminum block that simulates all the payload we might want to carry in the future. It also has on board a 721 gram battery. So this platform has a thrust to weight ratio which is greater than 2.7 and this is important. If you maximize the trust to weight ratio, you essentially maximize the acceleration as we've seen before. In addition to the processors, you also have to carry sensors. Here we show two sets of sensors that we commonly use in our laboratory: A laser scanner, and the laser scanner weighs about 270 grams. A camera system that weighs about 80 grams. And you also wanna think about power consumption and there are two sources of power consumption. First, the device itself consumes power. It's roughly 10 watts for a laser scanner and 1.5 watts for the scanner system. But in edition you're carrying these two payloads. The fact that you're carrying a 270 gram laser scanner means you're burning roughly 50 to 60 watts of power. LIkewise the fact you're carrying an 80 gram camera means you're burning roughly 15 watts of power. So you wanna think about how heavy a sensor is, You also wanna see its range. And all of those play into how fast the vehicle actually can go because longer the range, longer the stopping distance can be. You can detect obstacles far away, and therefore you have more time to come to a stop if you see an obstacle in front of you. This in turn allows you to go at a higher speed. Of course, longer the range, the heavier your sensor might be, and that in turn increases the weight of the platform. While it increases the weight of the platform, it'll decrease your trust to rate ratio. So this in an interesting design space to explore. So here are some examples of platforms we've built and tested in our laboratory in the last year. All these platforms are autonomous, they're different sizes, they carry different sensors. They weigh different amounts and they consume different amounts of power.